{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88af7f6b-da5c-4809-ac59-451c5de0da16",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">六 蝴蝶图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27e4c12-48d1-4a42-a3d2-6bf2188be61f",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## 筛选专营店的订单数据，选其中2024年Q4季度和2025年Q1季度的订单数量作蝴蝶图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4a96bd2-f353-45d1-9f2a-bd7374d23340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各专营店订单数量统计:\n",
      "年份季度   2024Q4  2025Q1\n",
      "店铺名称                 \n",
      "专营店1        5      10\n",
      "专营店10       5      10\n",
      "专营店2        4       5\n",
      "专营店3        2       7\n",
      "专营店4        8       6\n",
      "专营店5        6       7\n",
      "专营店6        6      10\n",
      "专营店7        4       7\n",
      "专营店8        2       9\n",
      "专营店9        7       8\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "数据汇总:\n",
      "2024年Q4季度总订单数: 49\n",
      "2025年Q1季度总订单数: 79\n",
      "专营店数量: 10\n",
      "数据时间范围: 2024-10-20 23:35:57 到 2025-10-23 21:19:32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "# 读取ERP订单数据\n",
    "data = pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "\n",
    "# 1. 处理时间与季度\n",
    "# 根据您的数据结构，日期列可能是第5列（索引4），请根据实际情况调整列名\n",
    "date_column = data.columns[6]  # 调整为您数据中实际日期列的列名\n",
    "\n",
    "# 转换时间格式并处理错误\n",
    "data[\"下单时间\"] = pd.to_datetime(data[date_column], errors=\"coerce\")\n",
    "data = data.dropna(subset=[\"下单时间\"])\n",
    "\n",
    "# 提取年份和季度\n",
    "data[\"年份\"] = data[\"下单时间\"].dt.year\n",
    "data[\"季度\"] = data[\"下单时间\"].dt.quarter\n",
    "data[\"年份季度\"] = data[\"年份\"].astype(str) + \"Q\" + data[\"季度\"].astype(str)\n",
    "\n",
    "# 2. 定义目标季度范围\n",
    "target_quarters = [\"2024Q4\", \"2025Q1\"]\n",
    "\n",
    "# 3. 提取专营店数据（只包含\"专营店\"的行）\n",
    "zhuanying_data = data[data['店铺名称'].str.contains('专营店', na=False)]\n",
    "\n",
    "# 4. 筛选目标季度的数据\n",
    "target_data = zhuanying_data[zhuanying_data[\"年份季度\"].isin(target_quarters)]\n",
    "\n",
    "# 5. 按专营店和季度统计订单数量\n",
    "quarterly_counts = target_data.groupby(['店铺名称', '年份季度']).size().unstack(fill_value=0)\n",
    "\n",
    "# 确保两个季度都有列（即使某个季度没有数据）\n",
    "for quarter in target_quarters:\n",
    "    if quarter not in quarterly_counts.columns:\n",
    "        quarterly_counts[quarter] = 0\n",
    "\n",
    "# 按店铺名称排序\n",
    "quarterly_counts = quarterly_counts.sort_index()\n",
    "\n",
    "# 创建数据数组\n",
    "stores = quarterly_counts.index.tolist()\n",
    "q4_2024 = quarterly_counts.get(\"2024Q4\", pd.Series([0]*len(stores), index=stores)).values\n",
    "q1_2025 = quarterly_counts.get(\"2025Q1\", pd.Series([0]*len(stores), index=stores)).values\n",
    "\n",
    "print(\"各专营店订单数量统计:\")\n",
    "print(quarterly_counts)\n",
    "\n",
    "# 创建图形和轴\n",
    "fig, ax = plt.subplots(figsize=(14, 10))\n",
    "\n",
    "# 设置深蓝色背景\n",
    "fig.patch.set_facecolor('#1a1a3d')\n",
    "ax.set_facecolor('#1a1a3d')\n",
    "\n",
    "# 设置y轴位置\n",
    "y_pos = np.arange(len(stores))\n",
    "\n",
    "# 设置条形图宽度\n",
    "bar_width = 0.35\n",
    "\n",
    "# 创建蝴蝶图 - 2025Q1在左侧（负值），2024Q4在右侧（正值）\n",
    "# 左侧：2025年Q1季度（红色）\n",
    "bars_left = ax.barh(y_pos, -q1_2025, bar_width, \n",
    "                   label='2025年Q1季度', color='#e74c3c', alpha=0.8)\n",
    "\n",
    "# 右侧：2024年Q4季度（蓝色）\n",
    "bars_right = ax.barh(y_pos, q4_2024, bar_width, \n",
    "                    label='2024年Q4季度', color='#3498db', alpha=0.8)\n",
    "\n",
    "# 设置y轴在中间\n",
    "ax.spines['left'].set_position('center')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "\n",
    "# 设置y轴标签（店铺名称）\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(stores, color='white', fontsize=11)\n",
    "\n",
    "# 设置x轴\n",
    "ax.set_xlabel('订单数量', color='white', fontsize=12, labelpad=15)\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "\n",
    "# 设置x轴刻度，显示绝对值\n",
    "max_value = max(max(q4_2024), max(q1_2025))\n",
    "x_ticks = np.arange(0, max_value + 5, 5)  # 每5个订单一个刻度\n",
    "ax.set_xticks(np.concatenate([-x_ticks[1:][::-1], x_ticks]))\n",
    "ax.set_xticklabels([str(int(x)) for x in np.concatenate([x_ticks[1:][::-1], x_ticks])])\n",
    "\n",
    "# 设置标题\n",
    "ax.set_title('各专营店2024年Q4季度 vs 2025年Q1季度内部订单销量对比', \n",
    "             color='white', fontsize=16, pad=20, fontweight='bold')\n",
    "\n",
    "# 添加图例\n",
    "ax.legend(facecolor='#1a1a3d', edgecolor='white', labelcolor='white', \n",
    "          loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
    "\n",
    "# 在条形图上添加数值标签\n",
    "for bar in bars_left:\n",
    "    width = -bar.get_width()  # 取绝对值\n",
    "    if width > 0:\n",
    "        ax.text(bar.get_width() - 0.3, bar.get_y() + bar.get_height()/2, \n",
    "                f'{int(width)}', \n",
    "                ha='right', va='center', color='white', fontsize=10, fontweight='bold')\n",
    "\n",
    "for bar in bars_right:\n",
    "    width = bar.get_width()\n",
    "    if width > 0:\n",
    "        ax.text(bar.get_width() + 0.3, bar.get_y() + bar.get_height()/2, \n",
    "                f'{int(width)}', \n",
    "                ha='left', va='center', color='white', fontsize=10, fontweight='bold')\n",
    "\n",
    "# 添加垂直网格线\n",
    "ax.grid(True, axis='x', alpha=0.2, color='white', linestyle='--')\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# 在中间添加垂直线\n",
    "ax.axvline(x=0, color='white', alpha=0.5, linewidth=1)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(bottom=0.15)  # 为图例留出空间\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 可选：保存图表\n",
    "# plt.savefig('专营店订单销量对比_蝴蝶图.png', dpi=300, facecolor='#1a1a3d', bbox_inches='tight')\n",
    "\n",
    "# 打印数据汇总\n",
    "print(f\"\\n数据汇总:\")\n",
    "print(f\"2024年Q4季度总订单数: {sum(q4_2024)}\")\n",
    "print(f\"2025年Q1季度总订单数: {sum(q1_2025)}\")\n",
    "print(f\"专营店数量: {len(stores)}\")\n",
    "print(f\"数据时间范围: {data['下单时间'].min()} 到 {data['下单时间'].max()}\")"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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